Pia’s Tidy Tuesday

library(readr)
library(dplyr)

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library(tidyverse)

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library(ggplot2)
library(janitor)

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library(plotly)

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library(gganimate)

whr <- read_csv("world-happiness-report.csv")

## Rows: 1949 Columns: 11

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## Delimiter: ","
## chr  (1): Country name
## dbl (10): year, Life Ladder, Log GDP per capita, Social support, Healthy lif...

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countries_plot <- whr %>% 
  clean_names() %>% 
  filter(country_name %in% c("Colombia", "Philippines", "China")) %>% 
  group_by(country_name) %>% 
  summarise(average_gdp = mean(log_gdp_per_capita)) %>% 
  ggplot(aes(x = country_name, 
             y = average_gdp,
             fill = country_name)) +
  geom_col() +
  labs(title = "2016-2020 Average GDP per Capita in Adeline, Marcela, and Pia's Countries",
       x = "",
       y = "") +
  theme_minimal() +
  theme(legend.position = "none")

ggplotly(countries_plot)